How managers’ green transformational leadership affects green resilient supply chain: The moderating impact of green ambidexterity and green innovation
Bibliographic record
Abstract
Upstream textile companies in Indonesia are the most significant contributors to environmentally hazardous production waste. Green Transformational Leadership (GTL) is critical to achieving a Green Resilient Supply Chain (GRS) to address vulnerability to supply chain disruptions while maintaining environmentally friendly practices. In addition, the mediation of Green Ambidexterity (GAM) and Green Innovation (GIN) is believed to strengthen the achievement of GRS. In line with the Indonesian government's policy to protect the environment, this study examines the direct effect of green transformational leadership variables on a green resilient supply chain. It evaluates Green Ambidexterity and Green Innovation's mediating effect on the relationship between GTL and GRS. This study analyzes the data of 50 production managers of upstream textile companies from 87 respondents collected from the survey. The analysis uses PLS-SEM based on variance to verify the relationship between variables. The research results indicate that green transformational leadership significantly influences green resilient supply chains. It was found that both Green Ambidexterity and Green Innovation significantly influence the Green Resilient Supply Chain, both directly and through mediating effects. Therefore, managers should consider implementing a Green Resilient Supply Chain, Green Ambidexterity, and Green Innovation practices to improve organizational goals while maintaining a green environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".